用情绪和时间特征预测社交媒体互动，点赞预测效果远超评论。
Predicting Social Media Engagement from Emotional and Temporal Features
- 基于情感与时间特征，用梯度提升模型预测互动量
- 点赞预测R²达0.98，评论预测仅0.41，差距显著
- 适合研究用户行为建模与内容传播机制的学者
我们提出一种机器学习方法，通过情感与时间特征预测社交媒体互动（评论与点赞）。数据集包含600首歌曲，标注了效价、唤醒度及相关情感指标。采用基于HistGradientBoostingRegressor的多目标回归模型，对对数转换后的互动比率进行训练，以应对目标分布偏斜问题。评估使用自定义的数量级准确率及标准回归指标（包括决定系数R²）。结果表明，情感与时间元数据结合现有观看次数，能有效预测未来互动。模型在点赞预测上达到R² = 0.98，但评论预测仅R² = 0.41。这一差距说明，点赞主要受可捕捉的情绪与曝光信号驱动，而评论还依赖当前特征集未涵盖的额外因素。
原文摘要 · Abstract (English)
We present a machine learning approach for predicting social media engagement (comments and likes) from emotional and temporal features. The dataset contains 600 songs with annotations for valence, arousal, and related sentiment metrics. A multi target regression model based on HistGradientBoostingRegressor is trained on log transformed engagement ratios to address skewed targets. Performance is evaluated with both a custom order of magnitude accuracy and standard regression metrics, including the coefficient of determination (R^2). Results show that emotional and temporal metadata, together with existing view counts, predict future engagement effectively. The model attains R^2 = 0.98 for likes but only R^2 = 0.41 for comments. This gap indicates that likes are largely driven by readily captured affective and exposure signals, whereas comments depend on additional factors not represented in the current feature set.
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